Dummy maps for linear effects of stimlus intensity

Model (6cond_with highlowcue and rampup/rampdown onsets)
parameter:
Table of Contents
Dummy maps for linear effects of stimlus intensity Pain only dummy maps Linear effects of stimlus intensity (low < med < high) Pain only :: load dataset Pain only :: check data coverage Pain only :: Plot diagnostics, before l2norm Pain only :: run robfit Pain only :: remove outliers based on plot Pain only :: plot diagnostics, after l2norm Pain only :: ttest Pain only :: Neurosynth similarity Vicarious only dummy maps Linear effects of stimlus intensity (low < med < high) Vicarious only :: load dataset Vicarious only :: check data coverage Vicarious only :: Plot diagnostics, before l2norm Vicarious only :: run robfit Vicarious only :: remove outliers based on plot Vicarious only :: plot diagnostics, after l2norm Vicarious only :: ttest Vicarious only :: Neurosynth similarity Cognitive only dummy maps Linear effects of stimlus intensity (low < med < high) Cognitive only :: load dataset Cognitive only :: check data coverage Cognitive only :: Plot diagnostics, before l2norm Cognitive only :: run robfit Cognitive only :: remove outliers based on plot Cognitive only:: plot diagnostics, after l2norm Cognitive only :: ttest Cognitive only :: Neurosynth similarity Manipulation check Rating motor contrast motor only :: load dataset motor only :: check data coverage motor only :: Plot diagnostics, before l2norm motor only :: run robfit motor only :: remove outliers based on plot motor only:: plot diagnostics, after l2norm motor only :: ttest motor only :: Neurosynth similarity

Pain only dummy maps

Linear effects of stimlus intensity (low < med < high)

Pain only :: load dataset

clear all;
close all;
 
contrast_of_interest = 'P_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'P_simple_STIM_stimlin_high_gt_low'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 3.306831 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6858907 Bit rate: 22.71 bits

Pain only :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:10:58 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

Pain only :: Plot diagnostics, before l2norm

drawnow; snapnow
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 5 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 22.22% Expected 3.60 outside 95% ellipsoid, found 9 Potential outliers based on mahalanobis distance: Bonferroni corrected: 5 images Cases 16 29 37 44 70 Uncorrected: 9 images Cases 12 16 29 34 37 43 44 48 70 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 50.00% Expected 3.60 outside 95% ellipsoid, found 3 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 3 images Cases 12 20 33 Mahalanobis (cov and corr, q<0.05 corrected): 5 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 3 4.1667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 4 5.5556 mahal_cov_uncor 9 12.5 mahal_cov_corrected 5 6.9444 mahal_corr_uncor 3 4.1667 mahal_corr_corrected 0 0 Overall_uncorrected 11 15.278 Overall_corrected 5 6.9444
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:11:22 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Pain only :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;
 

Pain only :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 5 participants, size is now 67
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…"
disp(n);
{'sub-0051'} {'sub-0074'} {'sub-0084'} {'sub-0093'} {'sub-0129'}

Pain only :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain only :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:11:27 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.037959 Image 1 34 contig. clusters, sizes 1 to 75546 Positive effect: 75480 voxels, min p-value: 0.00000000 Negative effect: 323 voxels, min p-value: 0.00000346
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:11:28 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .001, 'fdr');
Image 1 FDR q < 0.001 threshold is 0.000530 Image 1 38 contig. clusters, sizes 1 to 52260 Positive effect: 52891 voxels, min p-value: 0.00000000 Negative effect: 36 voxels, min p-value: 0.00000346
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:11:29 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 1296 voxels displayed, 51631 not displayed on these slices sagittal montage: 1341 voxels displayed, 51586 not displayed on these slices sagittal montage: 1272 voxels displayed, 51655 not displayed on these slices axial montage: 8999 voxels displayed, 43928 not displayed on these slices axial montage: 9984 voxels displayed, 42943 not displayed on these slices
drawnow, snapnow;
 
write(fdr_t, 'fname', strcat('/Users/h/Desktop/', contrast_of_interest, '_fdr_t.nii'), 'overwrite');
Writing: /Users/h/Desktop/P_simple_STIM_stimlin_high_gt_low_fdr_t.nii

Pain only :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ _________________ -0.15847 {'judgment' } 0.28108 {'motor' } -0.15445 {'memory' } 0.26202 {'movements' } -0.14742 {'correct' } 0.23881 {'sensorimotor' } -0.14144 {'pair' } 0.23154 {'muscle' } -0.13736 {'knowledge' } 0.22846 {'finger' } -0.13654 {'recognition'} 0.22582 {'hand' } -0.13637 {'semantic' } 0.22525 {'somatosensory'} -0.1328 {'sentence' } 0.19578 {'tapping' } -0.13244 {'person' } 0.19534 {'sensory' } -0.13187 {'decision' } 0.19366 {'stimulation' }
% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');
Pain only :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0314 4.7419 0.0000 1.0000 *** Cog Wholebrain 0.0183 4.8386 0.0000 1.0000 *** Emo Wholebrain -0.0461 -8.0049 0.0000 1.0000 ***
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.031372 0.0066058 4.7492 1.0314e-05 0.5597 {'Cog Wholebrain' } 0.018276 0.0037771 4.8387 7.3651e-06 0.57024 {'Emo Wholebrain' } -0.046062 0.005743 -8.0206 1.5316e-11 -0.94524
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [10.0024 11.0024 12.0024]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.030659 0.0061205 5.0092 3.8476e-06 0.59034 {'Cog Wholebrain' } 0.016462 0.0033724 4.8815 6.261e-06 0.5753 {'Emo Wholebrain' } -0.044541 0.005265 -8.4598 2.3476e-12 -0.99699
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [13.0024 14.0024 17.0013]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Vicarious only dummy maps

Linear effects of stimlus intensity (low < med < high)

Vicarious only :: load dataset

clear all;
close all;
 
contrast_of_interest = 'V_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'V_simple_STIM_stimlin_high_gt_low'
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 3.270726 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6889156 Bit rate: 22.72 bits

Vicarious only :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:12:11 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions
ans = 1×1 cell array
{1×1 region}

Vicarious only :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 40.28% Expected 3.60 outside 95% ellipsoid, found 4 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 48 Uncorrected: 4 images Cases 16 48 54 56 Retained 13 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 45.83% Expected 3.60 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 2 2.7778 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 4.1667 mahal_cov_uncor 4 5.5556 mahal_cov_corrected 1 1.3889 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 6.9444 Overall_corrected 3 4.1667
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:12:42 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Vicarious only :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious only :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0051" "participants that are outliers:... sub-0084" "participants that are outliers:... sub-0098"
disp(n);
{'sub-0051'} {'sub-0084'} {'sub-0098'}

Vicarious only :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious only :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:12:48 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.008747 Image 1 115 contig. clusters, sizes 1 to 10722 Positive effect: 4424 voxels, min p-value: 0.00000000 Negative effect: 13042 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:12:49 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 231 voxels displayed, 17235 not displayed on these slices sagittal montage: 243 voxels displayed, 17223 not displayed on these slices sagittal montage: 213 voxels displayed, 17253 not displayed on these slices axial montage: 3131 voxels displayed, 14335 not displayed on these slices axial montage: 3425 voxels displayed, 14041 not displayed on these slices
drawnow, snapnow;

Vicarious only :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _____________ __________ _____________ -0.36331 {'visual' } 0.23313 {'affect' } -0.35559 {'spatial' } 0.22889 {'emotion' } -0.35097 {'motion' } 0.21753 {'sentences'} -0.32147 {'shape' } 0.21737 {'rating' } -0.29764 {'eye' } 0.21553 {'verb' } -0.29577 {'object' } 0.2104 {'affective'} -0.28306 {'objects' } 0.20392 {'sentence' } -0.27963 {'position' } 0.20354 {'verbal' } -0.27805 {'attention'} 0.20132 {'neutral' } -0.27277 {'hand' } 0.20102 {'reward' }

Vicarious only :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0083 -1.1620 0.2491 0.0000 Cog Wholebrain -0.0050 -0.9940 0.3236 0.0000 Emo Wholebrain 0.0124 1.6172 0.1103 0.0000
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _______ ________ {'Pain Wholebrain'} -0.0083493 0.0071956 -1.1603 0.2498 -0.13675 {'Cog Wholebrain' } -0.004986 0.005013 -0.99461 0.3233 -0.11722 {'Emo Wholebrain' } 0.012368 0.007648 1.6172 0.11028 0.19058
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [10.0026 11.0026 12.0026]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ ________ ________ {'Pain Wholebrain'} -0.0093508 0.0072392 -1.2917 0.20066 -0.15223 {'Cog Wholebrain' } -0.0049508 0.004798 -1.0319 0.30564 -0.12161 {'Emo Wholebrain' } 0.01352 0.0076287 1.7723 0.080642 0.20886
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [13.0026 14.0026 17.0015]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Cognitive only dummy maps

Linear effects of stimlus intensity (low < med < high)

Cognitive only :: load dataset

clear all;
close all;
 
contrast_of_interest = 'C_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'C_simple_STIM_stimlin_high_gt_low'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 3.292915 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6891280 Bit rate: 22.72 bits
 

Cognitive only :: check data coverage

m = mean(con_data_obj);
% m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:13:36 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

Cognitive only :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 31.94% Expected 3.60 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 1 36 63 Uncorrected: 6 images Cases 1 16 36 44 63 68 Retained 19 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 48.61% Expected 3.60 outside 95% ellipsoid, found 4 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 4 images Cases 21 26 29 64 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 1 1.3889 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 2.7778 mahal_cov_uncor 6 8.3333 mahal_cov_corrected 3 4.1667 mahal_corr_uncor 4 5.5556 mahal_corr_corrected 0 0 Overall_uncorrected 10 13.889 Overall_corrected 3 4.1667
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:14:07 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

Cognitive only :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive only :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0014" "participants that are outliers:... sub-0082" "participants that are outliers:... sub-0120"
disp(n);
{'sub-0014'} {'sub-0082'} {'sub-0120'}

Cognitive only:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive only :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:14:12 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.009570 Image 1 113 contig. clusters, sizes 1 to 6694 Positive effect: 3849 voxels, min p-value: 0.00000000 Negative effect: 15259 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:14:13 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 687 voxels displayed, 18421 not displayed on these slices sagittal montage: 546 voxels displayed, 18562 not displayed on these slices sagittal montage: 717 voxels displayed, 18391 not displayed on these slices axial montage: 3822 voxels displayed, 15286 not displayed on these slices axial montage: 4090 voxels displayed, 15018 not displayed on these slices
drawnow, snapnow;
 
 

Cognitive only :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ___________________ __________ ________________ -0.32574 {'default' } 0.30577 {'visual' } -0.32101 {'person' } 0.25916 {'eye' } -0.30918 {'trait' } 0.25088 {'letter' } -0.28535 {'self' } 0.23991 {'target' } -0.28381 {'personal' } 0.23351 {'maintenance' } -0.27799 {'restingstate' } 0.22489 {'saccade' } -0.268 {'social' } 0.22083 {'movements' } -0.25312 {'rating' } 0.21908 {'visuospatial'} -0.25137 {'selfreferential'} 0.21773 {'execution' } -0.25087 {'negative' } 0.2137 {'numbers' }
Cognitive only :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0079 -1.4500 0.1515 0.0000 Cog Wholebrain -0.0214 -4.5034 0.0000 1.0000 *** Emo Wholebrain 0.0267 4.4589 0.0000 1.0000 ***
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.007895 0.0054386 -1.4517 0.151 -0.17108 {'Cog Wholebrain' } -0.021446 0.00476 -4.5054 2.5449e-05 -0.53097 {'Emo Wholebrain' } 0.02668 0.0059818 4.4602 3.0012e-05 0.52564
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [10.0027 11.0027 12.0027]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.0071976 0.0053391 -1.3481 0.18191 -0.15888 {'Cog Wholebrain' } -0.02087 0.0046224 -4.5149 2.4575e-05 -0.53209 {'Emo Wholebrain' } 0.026123 0.005877 4.445 3.1723e-05 0.52384
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [13.0027 14.0027 17.0016]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
 

Manipulation check Rating

motor contrast

motor only :: load dataset

clear all;
close all;
 
contrast_of_interest = 'motor'
contrast_of_interest = 'motor'
 
 
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
contrast_name = {
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'motor',... %motor
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
 
};
 
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead. loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 28753056 bytes Loading image number: 72 Elapsed time is 3.244472 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 6888883 Bit rate: 22.72 bits

motor only :: check data coverage

m = mean(con_data_obj);
% m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:14:58 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions

motor only :: Plot diagnostics, before l2norm

drawnow; snapnow;
 
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 40.28% Expected 3.60 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 8 images Cases 8 13 16 18 21 34 40 46 Retained 6 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.67% Expected 3.60 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 13 Uncorrected: 5 images Cases 13 38 58 63 67 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 4 5.5556 global_mean_to_variance 3 4.1667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.3889 mahal_cov_uncor 8 11.111 mahal_cov_corrected 0 0 mahal_corr_uncor 5 6.9444 mahal_corr_corrected 1 1.3889 Overall_uncorrected 12 16.667 Overall_corrected 2 2.7778
Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:15:30 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1 Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions Grouping contiguous voxels: 1 regions

motor only :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

motor only :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 72
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0044" "participants that are outliers:... sub-0088"
disp(n);
{'sub-0044'} {'sub-0088'}

motor only:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

motor only :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:15:36 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.041520 Image 1 8 contig. clusters, sizes 1 to 82870 Positive effect: 69078 voxels, min p-value: 0.00000000 Negative effect: 13838 voxels, min p-value: 0.00000000
orthviews(fdr_t);
Compressed NIfTI files are not supported. SPM12: spm_check_registration (v7759) 15:15:37 - 02/11/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/fmriprep20_template.nii.gz,1
drawnow, snapnow;
 
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects Compressed NIfTI files are not supported. sagittal montage: 2055 voxels displayed, 80861 not displayed on these slices sagittal montage: 2110 voxels displayed, 80806 not displayed on these slices sagittal montage: 1979 voxels displayed, 80937 not displayed on these slices axial montage: 15292 voxels displayed, 67624 not displayed on these slices axial montage: 16716 voxels displayed, 66200 not displayed on these slices
drawnow, snapnow;
 
 

motor only :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ______________ __________ ________________ -0.33957 {'trait' } 0.39796 {'movements' } -0.3256 {'positive' } 0.37099 {'execution' } -0.32456 {'negative' } 0.36466 {'hand' } -0.31252 {'age' } 0.34996 {'motor' } -0.30539 {'personal' } 0.32699 {'finger' } -0.2965 {'depression'} 0.31052 {'visual' } -0.29385 {'person' } 0.30197 {'sensorimotor'} -0.2933 {'emotion' } 0.28518 {'action' } -0.29102 {'disorder' } 0.28434 {'hands' } -0.2854 {'affect' } 0.27919 {'preparation' }
motor only :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
 
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0082 -1.8276 0.0718 0.0000 Cog Wholebrain 0.0299 11.6792 0.0000 1.0000 *** Emo Wholebrain -0.0190 -4.3232 0.0000 1.0000 ***
axis image
 
subplot(1, 3, 2)
 
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.008244 0.0045121 -1.8271 0.071892 -0.21532 {'Cog Wholebrain' } 0.029943 0.002562 11.687 2.2204e-15 1.3774 {'Emo Wholebrain' } -0.019015 0.0043979 -4.3236 4.9163e-05 -0.50955
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [10.0028 11.0028 12.0028]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
 
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
 
clear csim
for i = 1:3
 
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
 
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
 
subplot(1, 3, 3)
 
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.0041255 0.004078 -1.0117 0.31514 -0.11923 {'Cog Wholebrain' } 0.026864 0.002331 11.524 2.2204e-15 1.3582 {'Emo Wholebrain' } -0.020797 0.0040213 -5.1717 2.0547e-06 -0.60949
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [13.0028 14.0028 17.0017]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
 
% save html
% pubdir = pwd;
% pubfilename = '6cond_cueeffect_contrast.mlx';
%
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);